University Life in Udine

Do you want to move to Udine or nearby?

If you have decided to move to Udine or the municipalities in its province, you will find all the information you need here.

Some info about the notebook

In this notebook there will be content designed specifically for users, others for programmers.

This distinction is made explicit by the use of the following icons:

• User 🧍‍♂️
• Programmer 👨‍💻

---

Setup 👨‍💻

We import the necessary libraries for the notebook to correctly work.

import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import folium
from folium.plugins import MarkerCluster
import leafmap
import os
import sys

# Import custom modules
sys.path.insert(0, os.path.abspath('../')) # need to pass absolute path in nb

from functions.dataviz_folium import *

print("> Setup Completed")

> Setup Completed

Import Data 👨‍💻

We import the necessary data for analysis and representations.

First, we import the shape files.

# import data on Italian municipalities
italy_municipalities_path = "../data/Com01012021_g/Com01012021_g_WGS84.shp"
italy_municipalities = gpd.read_file(italy_municipalities_path)

# save Udine and the municipalities in the province of Udine
udine_geodf = italy_municipalities.loc[italy_municipalities["COMUNE"] == "Udine"]
udine_mun_geodf = italy_municipalities.loc[italy_municipalities["COD_PROV"] == 30]

It follows a simple plot.

# create the base plot with the municipalities
base = udine_mun_geodf.to_crs(epsg=4326).plot(
    figsize=(20, 10),
    color="#b5cea8",
    edgecolor="k",
    linewidth = 0.25
    )

# plot Udine hover its municipalities
udine_geodf.to_crs(epsg=4326).plot(
    facecolor='none', 
    ax=base,
    color="#EE6C4D",
    edgecolor="k",
    linewidth = 0.75
    )

<AxesSubplot:>

We now import the data about the house cost and apartment rents of the municipalities of Udine.

Source: https://www.immobiliare.it/mercato-immobiliare/friuli-venezia-giulia/udine-provincia/

The source is updated to December 2021.

udine_mun_house_cost = pd.read_csv("../data/udine_municipalities_house_cost.csv")
udine_mun_house_cost

	Municipality	Sale	Rent
0	Udine	1504	7.91
1	Aiello del Friuli	946	6.74
2	Amaro	613	5.90
3	Ampezzo	608	6.02
4	Aquileia	968	6.99
...	...	...	...
129	Venzone	613	5.92
130	Verzegnis	480	5.90
131	Villa Santina	649	5.90
132	Visco	700	6.40
133	Zuglio	462	5.90

134 rows × 3 columns

Let's add the data to the GeoDataFrame.

# check that the number of municipalities matches

print("Municipalities in GeoDataFrame:", len(udine_mun_geodf["geometry"]))
print("Municipalities in HouseCost df:", len(udine_mun_house_cost["Municipality"]))

Municipalities in GeoDataFrame: 134
Municipalities in HouseCost df: 134

The accounts add up, we can perform a join.

udine_mun_geodf_with_cost = pd.merge(udine_mun_geodf, udine_mun_house_cost, left_on='COMUNE', right_on='Municipality', how='left')
udine_mun_geodf_with_cost[["COMUNE","geometry","Municipality","Sale","Rent"]].head()

	COMUNE	geometry	Municipality	Sale	Rent
0	Aiello del Friuli	POLYGON ((839183.531 5090784.049, 839213.968 5...	Aiello del Friuli	946	6.74
1	Amaro	POLYGON ((815571.541 5148741.150, 816172.975 5...	Amaro	613	5.90
2	Ampezzo	POLYGON ((789780.844 5154195.205, 789796.845 5...	Ampezzo	608	6.02
3	Aquileia	POLYGON ((841232.696 5081056.617, 841292.009 5...	Aquileia	968	6.99
4	Arta Terme	POLYGON ((813118.877 5158452.582, 813153.940 5...	Arta Terme	663	5.90

Choropleth 🧍‍♂️

Let's create some visualizations.

Static Maps

We first create simple Choropleth based on the average price of houses and rentals in the province of Udine.

House Sale Prices

# create base plot with municipality color based on "Sale"
base = udine_mun_geodf_with_cost.to_crs(epsg=4326).plot(
    column='Sale',
    cmap="magma_r", 
    edgecolor="k", 
    linewidth=0.5,
    legend=True,
    figsize=(15,12)
    )

# make udine easily recognisable by adding its borders
udine_geodf.to_crs(epsg=4326).boundary.plot(
    facecolor='none', 
    ax=base,
    color="k",
    linewidth = 1.5
    )

base.set_axis_off()
base.set_title('House Sale Prices in the Province of Udine (EUR/m\u00b2)')
plt.show()

House Rent Prices

# create base plot with municipality color based on "Rent"
base = udine_mun_geodf_with_cost.to_crs(epsg=4326).plot(
    column='Rent',
    cmap="magma_r", 
    edgecolor="k", 
    linewidth=0.5,
    legend=True,
    figsize=(15,12)
    )

# make udine easily recognisable by adding its borders
udine_geodf.to_crs(epsg=4326).boundary.plot(
    facecolor='none', 
    ax=base,
    color="k",
    linewidth = 1.5
    )

base.set_axis_off()
base.set_title('House Rent Prices in the Province of Udine (EUR/m\u00b2)')
plt.show()

Interactive Maps

Now let's create an interactive version of the choropleths, facilitating the exploration of the territory.

Setup

# setup
udine_lat = 46.0609604
udine_lon = 13.1980551
list_of_layers = ['Stamen Terrain','cartodbpositron']

House Sale Prices

interactive_map = create_folium_map_choropleth(
    lat=udine_lat, 
    lon=udine_lon,
    list_of_layers=list_of_layers,
    geodf=udine_mun_geodf_with_cost,
    column="Sale"
    )

interactive_map

> Adding multiple layers
> Adding Choropleth
> Adding Tooltips and Popups
> Interactive Map Created

Make this Notebook Trusted to load map: File -> Trust Notebook

House Rent Prices

interactive_map = create_folium_map_choropleth(
    lat=udine_lat, 
    lon=udine_lon,
    list_of_layers=list_of_layers,
    geodf=udine_mun_geodf_with_cost,
    column="Rent"
    )

interactive_map

> Adding multiple layers
> Adding Choropleth
> Adding Tooltips and Popups
> Interactive Map Created

Make this Notebook Trusted to load map: File -> Trust Notebook

Save Maps

We save the maps as .html, so that they can then be incorporated into other websites.

interactive_map = create_folium_map_choropleth(
    lat=udine_lat, 
    lon=udine_lon,
    list_of_layers=list_of_layers,
    geodf=udine_mun_geodf_with_cost,
    column="Sale"
    )

interactive_map.save("../dataviz/interactive_map_house_sale.html")

interactive_map = create_folium_map_choropleth(
    lat=udine_lat, 
    lon=udine_lon,
    list_of_layers=list_of_layers,
    geodf=udine_mun_geodf_with_cost,
    column="Rent"
    )

interactive_map.save("../dataviz/interactive_map_house_rent.html")

> Adding multiple layers
> Adding Choropleth
> Adding Tooltips and Popups
> Interactive Map Created
> Adding multiple layers
> Adding Choropleth
> Adding Tooltips and Popups
> Interactive Map Created